Pushed based real-time analytics system

ABSTRACT

Configurations for providing a pushed based real-time analytics system are disclosed. The system incorporates a network and distributed database configured to stream real-time analytics data through multiple tiers. The real-time analytics data is routed/pushed through the system without requiring an additional requests from a front-end client. For instance, after the front-end client initiates a query with the real-time analytics system, a pipeline is opened to the front-end client and web site traffic data is pushed without requiring polling or other explicit requests from the front-end client. As incoming web site tracking data communications are received, the incoming web site tracking data communications are automatically routed and pushed through different tiers of the system. Real-time web site tracking data is automatically presented to the front-end client without requiring constant refreshing (which introduces latency) of a web browser or similar client application.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of U.S. patent application Ser. No. 13/239,270 entitled “PUSHED BASED REAL-TIME ANALYTICS SYSTEM,” filed on Sep. 21, 2011, which claims the benefit of priority under 35 U.S.C. § 119 from U.S. Provisional Patent Application Ser. No. 61/510,052 entitled “REAL-TIME ANALYTICS SYSTEM,” filed on Jul. 20, 2011, the disclosure of which is hereby incorporated by reference in its entirety for all purposes.

BACKGROUND

Web site analytics involves the analysis and reporting of web site traffic data to an associated publisher. A publisher is an entity that owns and/or manages a particular web site. The publisher can monitor web site analytics data related to user visits and links to the web site by using web site analytics tools provided by third parties.

Existing web site analytics data systems work well for processing a high volume of web traffic data in order to provide interactive reports over substantial periods of time with high fidelity. However, these systems incur a high level of latency from hit occurrence to hit reporting, and also do not allow reports to segment in a fine and granular time basis. Consequently, existing web site analytics data systems have difficulty in monitoring sudden spikes or drops in traffic, or for tracking, for example, a propagation of a link to the publisher's web site over a social networking site, which can typically unfold in a time period of minutes.

Furthermore, the high latency of web site analytics reporting makes it more difficult for publishers to implement web site analytics on their web sites. Because it takes a longer period of time for reports to show up, publishers also often fail to notice any erroneous deployment of web site analytics tracking code on associated web sites.

SUMMARY

The subject technology provides for pushing event tracking data through different tiers of servers in a real-time analytics system. The subject technology provides for receiving an event tracking data communication at a first server in which the event tracking data communication includes event tracking data for tracking an activity associated with a source of the event tracking data communication. The event tracking data communication is pushed from the first server to a real-time server in which the real-time server aggregates the event tracking data for reporting. A front-end client is identified for receiving the aggregated event tracking data. The aggregated event tracking data is then delivered to the identified front-end client for presentation.

Another aspect of the subject technology provides for receiving an event tracking data communication in which the event tracking data communication includes event tracking data for tracking an activity associated with a source of the event tracking data communication. The subject technology determines whether a tracking account associated with the event tracking data communication has an owner in which the owner comprises a real-time server assigned to process incoming event tracking data communications associated with the tracking account. An elapsed time period is determined since receiving the event tracking data communication if the owner comprises a different real-time server. The event tracking data communication is discarded if the elapsed time period is at least a threshold time period. The event tracking data communication is pushed to the different real-time server if the elapsed time period is below the threshold time period.

The subject technology further provides for receiving an event tracking data communication at a first server in which the event tracking data communication includes event tracking data for tracking an activity associated with a source of the event tracking data communication. The event tracking data communication is pushed from the first server to a real-time server in which the real-time server aggregates the event tracking data for reporting. The subject technology determines whether a tracking account associated with the event tracking data communication has a different owner than the real-time server. The different owner includes a different real-time server assigned to process incoming event tracking data communications associated with the tracking account. A front-end client is identified for receiving the aggregated event tracking data if the event tracking data communication does not have the different owner. The aggregated event tracking data is then delivered to the identified front-end client for presentation.

Additionally, the subject technology provides a system for pushing incoming event tracking data communications to another real-time server in order to meet a real-time deadline. The system includes a parser module configured to parse event tracking data included in an event tracking data communication to extract real-time analytics data. The event tracking data communication includes event tracking data for tracking an activity associated with a source of the event tracking data communication, and the source is a web site. The system further includes an ownership module configured to determine whether the extracted real-time analytics data has an associated owner in which the associated owner includes a real-time server assigned to process incoming event tracking data communications associated with the web site. Additionally, the system includes a request router module configured to route the event tracking data communication to a different real-time server if the associated owner is determined to be the different real-time server, to determine an elapsed time period since receiving the event tracking data communication if the associated owner comprises the different real-time server, to discard the event tracking data communication if the elapsed time period is at least a threshold time period, and to push the event tracking data communication to the different real-time server if the elapsed time period is below the threshold time period.

It is understood that other configurations of the subject technology will become readily apparent to those skilled in the art from the following detailed description, wherein various configurations of the subject technology are shown and described by way of illustration. As will be realized, the subject technology is capable of other and different configurations and its several details are capable of modification in various other respects, all without departing from the scope of the subject technology. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the subject technology are set forth in the appended claims. However, for purpose of explanation, several configurations of the subject technology are set forth in the following figures.

FIG. 1 illustrates an exemplary architecture diagram of a multi-tiered real-time analytics system of some configurations.

FIG. 2 conceptually illustrates an exemplary process for routing an event tracking data communication for persistent and temporary storage of some configurations.

FIG. 3 conceptually illustrates an exemplary process for processing an event tracking data communication in a real-time server of some configurations.

FIG. 4 conceptually illustrates different attributes and data structures that are included in some configurations of the multi-tiered real-time analytics system.

FIG. 5 conceptually illustrates an exemplary process for pushing event tracking data through different tiers of servers in a real-time analytics system of some configurations.

FIG. 6 conceptually illustrates an exemplary process for pushing event tracking data to a designated real-time server acting as an owner of some configurations.

FIG. 7 conceptually illustrates an exemplary software architecture of an real-time server of some configurations.

FIG. 8 conceptually illustrates an electronic system in which some configurations are implemented.

DETAILED DESCRIPTION

The detailed description set forth below is intended as a description of various configurations of the subject technology and is not intended to represent the only configurations in which the subject technology may be practiced. The appended drawings are incorporated herein and constitute a part of the detailed description. The detailed description includes specific details for the purpose of providing a thorough understanding of the subject technology. However, it will be clear and apparent to those skilled in the art that the subject technology is not limited to the specific details set forth herein and may be practiced without these specific details. In some instances, well-known structures and components are shown in block diagram form in order to avoid obscuring the concepts of the subject technology.

Current analytics systems provide for aggregating and reporting of web site traffic data with high latency that can range from several hours to over a day. To address these issues with high latency, a real-time analytics system for receiving and reporting web site traffic data with low latency is provided. More specifically, it is desirable to provide a substantially real-time presentation of the web site traffic data without introducing additional latency in the system by having a typical request/response/handshake communication flow.

The subject technology is directed to configurations for a push based system for web site traffic data collection and reporting. The system incorporates a network and distributed database configured to stream aggregated data through multiple tiers. The aggregated data is routed/pushed through the system based on primary key sorting. Bandwidth is maximized by one forwarded data between tiers when there is new data to report.

Once a front-end client initiates a query with the real-time analytics system, a pipeline is opened to the front-end client and web site traffic data is pushed without requiring polling or other explicit requests from the front-end client. As incoming web site tracking data communications are received by a collecting server, the incoming web site tracking data communications are automatically routed and pushed through different tiers of the system. At each tier in the system, the web site tracking data may be segmented, aggregated, and/or ranked before being forwarded on to the next tier. In this manner, real-time web site tracking data is automatically presented to the front-end client without requiring constant refreshing (which introduces latency) of a web browser or similar client application.

To use a web site analytics data system, a publisher typically provides tracking instructions embedded in the web site's web pages (alternatively or conjunctively, by providing code in its web page that requests the tracking instructions from an analytics server). Typically, the tracking instructions are a small region of JavaScript™ code that the publisher includes in each page of the publisher's web site for which traffic is to be tracked. When a web page is rendered by a user device and/or client (e.g., web browsers, mobile devices, tablet devices, thin clients, thick clients, etc.), the tracking instructions are executed, which collects visitor data related to one or more different user sessions and sends it back to a real-time analytics server in the form of an event tracking data communication for processing. One method of sending the event tracking data communication is to send it as part of a Hypertext Transfer Protocol (HTTP) request.

Web site traffic data can be in the form of the aforementioned event tracking data communications (e.g., Hypertext Transfer Protocol (HTTP) requests that are generated from various user devices and/or clients). Each event tracking data communication can be triggered upon loading of a web page or resource (e.g., image file, banner ad, audio file, etc.) on the web site by a particular visitor to a publisher's web site. For the purpose of web site analytics reporting, the associated event tracking data (e.g., included in associated HTTP requests) can include a web property ID (e.g., an account identifier that identifies a publisher's analytics account of a particular web site), visitor identity data (i.e., corresponding to a particular user session), an event statistic(s) (such as a timestamp of the current web page communication), and user data such as location data (i.e., geographical information) associated with the visitor to the publisher's web site. The amount and types of events/information that are reported in the tracking data can be specified by the publisher (e.g., to account for particular information that a publisher wishes to track as web site analytics data).

Although an event tracking data communication is described as including web site traffic data, an event tracking data communication is not limited to including web site traffic data. In particular, an event tracking data communication can include data from any source (e.g., application, platform, etc.) that for tracking an activity associated with the source. For example, in a mobile application platform, an event tracking data communication can include event tracking data that tracks an event associated with an activity within the mobile application platform. In this example, the event tracking data associated with the mobile application platform may not include web site traffic data because the mobile application platform does not require the use of a client such as a web browser. Aspects of the multi-tiered real-time analytics system therefore can be configured to receive and process an event tracking data communication(s) including different forms of event tracking data that are outside the scope of web site tracking data.

A multi-tiered real-time analytics system as described herein can be configured to receive and process incoming event tracking data communications as described above to enable real-time reporting of web site analytics data with low latency. As used herein, the term “real-time” corresponds with reporting web site analytics data within a specified deadline by pushing/transmitting the web site analytics data through different tiers of the multi-tiered real-time analytics system. The expression “low latency” as used herein can be therefore understood as a measurement of time that covers a time period from the reception of an event tracking data communication to the real-time reporting of the web site analytics data included therein. As illustrated in FIG. 1 and described in further detail below, the flow of event tracking data goes through several tiers for real-time web site analytics processing and reporting.

A real-time system is subject to a “real-time constraint” that can impose operational deadlines from an event to a system response. Thus, a real-time system operates within constraints on response time. In the context of the multi-tiered real-time analytics system, the flow of event tracking data through different tiers of the system is required to meet deadlines in order to report the data within a specified period of time (e.g., in a real-time manner with low latency). In one example, the real-time analytics system imposes a respective one second deadline for processing event tracking data within each tier of the system. For instance, upon receipt of an event tracking data communication, a logging server (e.g., in a logging tier described further below) must forward the event tracking data communication within a one second deadline to a real-time server (e.g., in a real-time analytics tier described below). In the event that the logging server can not meet the one second deadline, the event tracking data communication is dropped (e.g., discarded) by the logging server and no further processing occurs for that event tracking data communication. Similarly, when the real-time server receives the event tracking data communication from the logging server, the real-time server must forward the event tracking data communication to another real-time server or a front-end server (as described below) within a one second deadline. The real-time server similarly discards the event tracking data communication if it can not meet the one second deadline. The one second deadline discussed above is just one example of the specified period of time. Other periods of time may be specified. In addition, each tier of the real-time analytics system may have a different specified period of time for processing an event tracking data communication.

FIG. 1 illustrates an exemplary architecture diagram of a multi-tiered real-time analytics system 100 for receiving and reporting web site traffic data in the form of event tracking data communications. The multi-tiered real-time analytics system 100 receives such event tracking data communications from a network 105 and routes the received communications for persistent and temporary storage to enable web analytics processing by the system. The network 105 can include, but is not limited to, a local network, remote network, or an interconnected network of networks (e.g., Internet as shown in FIG. 1).

As illustrated, the multi-tiered real-time analytics system 100 may further include a collecting tier 110, a logging tier 120, a persistent tier 130, a real-time analytics tier 140 and a front-end tier 150. Each of the aforementioned tiers can include a respective cluster of servers/computers that perform a same set of functions in a distributed and/or load balanced manner. A cluster can be understood as a group of servers/computers that are linked together to seamlessly perform the same set of functions, which can provide performance, reliability and availability advantages over a single server/computer architecture. For the sake of simplicity, each illustrated tier depicts several number of servers. However, one of ordinary skill in the art would realize that the illustrated tiers could include any number of servers and still be within the scope of the subject technology. The descriptions of the various tiers of the system 100 are explained in further detail below.

In some configurations, the collecting tier 110 receives incoming event tracking data communications from the network 105. The collecting tier 110 includes at least one collecting server that is configured to receive and forward the event tracking data communication. Each collecting server of the collecting tier 110 can process an incoming event tracking data communication to determine a particular server in the logging tier 120 to route the event tracking data communication. In some implementations, the collecting server can utilize a load balancing technique to determine which logging server in the logging tier 120 to route the event tracking data communication. The load balancing technique can be based upon different factors such as server load, network load, and/or any other suitable computing metric for measuring resource utilization at a particular server.

The logging tier 120 can receive a routed event tracking data communication from a particular collecting server of the collecting tier 110. Upon receiving the event tracking data communication, the subsequent tasks of the logging tier 120 can be understood as being two-fold: 1) route received event tracking data communications to the persistent tier 130 for persistent storage and 2) route received event tracking data communication to the real-time analytics tier 140 for temporary storage.

To accomplish the aforementioned tasks, the logging tier 120 includes at least one logging server that is configured to receive the routed event tracking data communication from the collecting server. In some implementations, the logging server aggregates incoming event tracking data communications and then periodically routes the aggregated event tracking data communications to the persistent tier 130 for persistent storage. Each event tracking data communication is associated with a particular web site of a publisher and the aggregated event tracking data communications can originate from disparate associated web sites and/or the same web site. In some implementations, the logging server can be further configured to verify each routed event tracking data communication to guarantee delivery of the event tracking data communications to a particular long-term storage server in the persistent tier 130.

For persistent storage of the event tracking data communication, the persistent tier 130 of some configurations can include at least one long-term storage server. Each long-term storage server includes a non-volatile storage device(s), a database(s), or other similar non-volatile storage medium for persistent storage of an incoming event tracking data communication. Persistent storage as used herein can be understood as referring to a type of non-volatile storage that stores the event tracking data in a reliable manner. In some configurations, persistent storage can include lossless or near-lossless storage in which the data is stored without being deleted and/or replaced at a subsequent time period. Additionally, persistent storage can be characterized as storing event tracking data in a form of historical records so that the system can extract the stored tracking data at a later period of time. In some configurations, the stored event tracking data can be modified at a later time period. Alternatively, the stored event tracking data can be in the form of immutable records (i.e., data that is not modified after being stored).

To improve the reliability of the persistent storage, some implementations of the persistent tier 130 can replicate the stored event tracking data across another long-term storage server so that the tracking data is mirrored in at least one other long-term storage server. For instance, the long-term storage server that receives a particular event tracking data communication can then undergo a replication process (either initiated by the long-term storage server itself, a second long-term storage server, or another server in the multi-tiered system) to copy (i.e., replicate) the particular event tracking data to another long-term storage server. Data redundancy can therefore can be provided to enable fault tolerance in the event the data stored at one long-term storage server is corrupted and/or lost.

To meet the low latency requirements for a multi-tiered real-time analytics system, the system can employ a lossy scheme for reporting web site traffic data in which some loss of event tracking data is tolerated. In particular, because the event tracking data is stored in persistent storage as described above, the multi-tiered real-time analytics system can adopt certain trade-offs for the sake of minimizing latency when routing the event tracking data to the real-time analytics tier as described further below.

Each logging server of the logging tier 120 is further configured to route event tracking data communications to the real-time analytics tier 140 for temporary storage. Given the requirement of low latency (i.e., minimizing latency in the system) connected with reporting real-time analytics data, each logging server can be configured to route the event tracking data communications to the real-time analytics tier 140 at a greater frequency than routing the communications to the persistent tier 130. In some implementations, routing the tracking data communications to the real-time analytics tier 140 can be understood as occurring asynchronously with routing the communications to the persistent tier 130.

The real-time analytics tier 140 includes at least one real-time server that is configured to receive the routed event tracking data communication from the logging tier 120. The real-time server in some implementations is configured to aggregate and/or sort the event tracking data according to one or more predetermined criteria for real-time reporting of the web site analytics data to a publisher. As described before, each event tracking data communication can include visitor identity data corresponding to a user session, location data associated with the user/visitor, and a timestamp corresponding to the visitor action.

The real-time server of some implementations can process the aforementioned data to generate real-time analytics data for temporary storage. For instance, the real-time server can aggregate and sort the location data according to the timestamp of the visitor action and then temporarily store the aggregated and/or sorted real-time analytics data. One of ordinary skill in the art would appreciate that other criteria can be used for aggregating and sorting the real-time analytics data. In one aspect, the real-time server sends the aggregated and/or sorted real-time analytics data to the front-end tier 150 for reporting the real-time analytics data. Moreover, as used herein, temporary storage can be understood as providing volatile, quick access to data (in comparison to persistent storage) stored in a storage device such as volatile memory (e.g., random access memory).

Alternatively to the above, the front-end tier 150 in one aspect can run queries on the stored real-time analytics data to report to the publisher with low latency. For instance, the real-time server can be configured to process incoming queries from the front-end tier on the stored real-time analytics data. The real-time server can return matching real-time analytics data based on the queries to the front-end tier 150 for real-time presentation with low latency.

In some implementations, the system 100 includes the front-end tier 150, which includes at least one front-end server configured to report the real-time analytics data with low latency. As described above, the front-end server of some implementations is configured to receive real-time analytics data from the real-time server. By way of example, the front-end server can receive real-time web analytics data associated with a particular location (e.g., city, state, country, etc.) over a predetermined time period for presentation at a front-end interface (i.e., graphical user interface) to an end-user. One of ordinary skill in the art would understand that any set of real-time analytics data could be received for reporting the analytics data in a real-time manner.

Although the above description provides that system 100 includes several tiers, one of ordinary skill in the art would appreciate that in some configurations respective tiers could be combined to function as a single tier and still be within the scope of the subject technology. For instance, the above described collecting and logging tiers could be implemented as a single tier that perform a combined set of functions.

Moreover, to facilitate communication between the respective tiers as described above, the respective tiers can be configured to communication over a type of network, such as a local area network, a wide area network, or an interconnected network of networks (e.g., the Internet) and by using any sort of network/communications protocol (Transmission Control Protocol and Internet Protocol (TCP/IP), HTTP, etc.).

In addition, different configurations of the multi-tiered system can be organized in different ways. For instance, different tiers could be located in different geographical locations. Each geographical location can be associated with a respective data center that includes one or more servers associated with a particular tier. Alternatively or conjunctively, the respective servers in one particular tier could be located in one or more different geographical locations and/or data centers. This flexibility in organizing the multi-tiered system provides an advantage of increasing the scalability of the system to include servers across potentially disparate physical locations.

In the following section, respective exemplary processes for routing an event tracking data communication to a real-time server and processing the tracking data communication by the real-time server are described in further detail.

FIG. 2 conceptually illustrates an exemplary process 200 for routing an event tracking data communication for persistent and temporary storage of some configurations. More specifically, FIG. 2 illustrates the process performed by a logging server to route event tracking data communications to a persistent tier and to a real-time analytics tier. The process 200 of FIG. 2 will be described by reference to corresponding portions of FIG. 1.

The process 200 starts when an event tracking data communication is received at 205. By reference to FIG. 1, the logging server in the logging tier 120 receives the event tracking data communication from a collecting server in the collecting tier 110. In some implementations, the logging server can receive the web site communication when pushed from the collecting server. For example, instead of the logging server utilizing a client pull technique to request an event tracking data communication, the tracking data communication is pushed as a server push from the collecting server to the logging server. Hence, the latency of receiving the event tracking data communication is decreased because the aforementioned data flow reduces the typical overhead associated with a traditional request/response communication flow.

The process 200 can then perform the task of routing the received web site to separate tiers. At 210, the event tracking data communication is routed to a long-term storage server (e.g., in the persistent tier 130 from FIG. 1) for persistent storage. Referring to FIG. 1, the logging server in the logging tier 120 routes the event tracking data communication to the long-term storage server in persistent tier 130. In some implementations, the logging server can queue several received event tracking data communications for routing the tracking data communications in a batch manner. When the logging server performs the steps in the process 200, some implementations can further verify the delivery of the event tracking data communication to the long-term storage server in order to guarantee the delivery of the communication.

The process 200 then continues to 215 to route the event tracking data communication to a selected real-time server. Referring to FIG. 1, the real-time analytics tier 140 can include several real-time servers. The logging server in the logging tier 130 can arbitrarily select a real-time server to route the event tracking data communication. By performing an arbitrary selection of the real-time server, latency in reporting real-time analytics data can be decreased because the process 200 does not rely on any particular real-time server to process the tracking data communication. The arbitrary selection of the real-time server by the process 200 also alleviates potential processing bottlenecks by impartially distributing the event tracking data communications across the real-time servers in the real-time analytics tier 140 from FIG. 1. In some configurations, the real-time server can be selected based on different criteria. For example, the logging server can select the real-time server using a randomized selection in which a particular real-time server is randomly selected from among more than one different real-time servers. In yet other configurations, the logging server can select the real-time server based on load-balancing requirements in which one or more particular computing metrics are utilized to determine the real-time server to receive the event tracking data communication.

Additionally, the process 200 at 215 can route the event tracking data communication at a greater frequency than routing the communication to the long-term storage server. For example, when the logging server receives the event tracking data communication, the logging server can queue several tracking data communications before sending them to the long-term storage server because low latency is not a factor for the aspect of the multi-tiered system dealing with persistent storage of the event tracking data. In one aspect, the logging server can immediately or in a shorter time period route the event tracking data communication to the randomly selected real-time analytics server to ensure that the requirement of low latency processing, storing and reporting of the real-time analytics data is met. After routing the event tracking data communication to the long-term storage server and the real-time server, the process 200 then ends.

Although the above description of the process 200 describes that routing to the long-term storage server occurs before routing to the real-time server, one of ordinary skill in the art can appreciate that the described order is one exemplar and some implementations could reverse the order of the previously described routing without departing from the scope of the invention. For instance, some implementations could route the event tracking data communication to the real-time server before routing the communication to the long-term storage server. In another aspect, routing the communication to the long-term storage server and the real-time server occurs in a simultaneous and/or synchronous manner.

The following discussion describes an exemplary process for processing and storing an event tracking data communication received by an arbitrarily selected real-time server. In some configurations, after the event tracking data communication is routed to the arbitrarily selected real-time server as described above in FIG. 2, the process illustrated in FIG. 3 is performed by the real-time server. More specifically, FIG. 3 conceptually illustrates an exemplary process 300 for processing an event tracking data communication in a real-time server.

The process 300 begins when a routed event tracking data communication is received at 305 in a selected real-time server. For instance, the real-time server can receive the event tracking data communication when the communication is pushed from the logging server to the real-time server. As similarly discussed above in connection with FIG. 2, the logging server can employ a push technique to push event tracking data communications to the arbitrarily selected real-time server. In other words, by pushing the event tracking data communication, the logging server initiates a communication flow without requiring an initial request from the recipient (i.e., the arbitrarily selected real-time server). Thus, an associated overhead of a typical request/request communication flow can be eliminated in the multi-tiered system. As a result, latency is reduced in the system for processing and reporting real-time analytics data.

At 310, the process 300 determines the real-time server(s) associated with the event tracking data communication. In some implementations, a real-time server(s) can be associated with a web site of a particular publisher. For instance, the real-time server can consult a table, list or other suitable data structure that indicates one or more real-time servers that are associated with the publisher's web site. The associated one or more real-time servers therefore can be considered an “owner” of any event tracking data communication for that publisher's web site. In practical terms, when acting as the owner, the associated real-time server is required to process each event tracking data communication for that publisher's web site. Based on the determination at 310, the process 300 can decide at 320 whether a different real-time server is associated with the received event tracking data communication.

In an instance in which the process 300 determines at 320 that the real-time server is not associated with the event tracking data communication, the process 300 continues to 330. At 330, the process 300 routes the event tracking data communication to one or more real-time servers associated with the event tracking data communication based on the determination at 310. Referring to FIG. 1, the real-time server can route the event tracking data communication to the associated one or more real-time servers in the real-time analytics tier 140. The process 300 then ends.

Alternatively, in an instance in which the process 300 determines at 320 that the real-time server is associated with the event tracking data communication, the process 300 continues to 340 to temporarily store real-time analytics data based on the event tracking data communication. A more detailed description of exemplars of the real-time analytics data will be discussed below in connection with FIG. 4 below. In some implementations, the process 300 temporarily stores the real-time analytics data in non-persistent storage (e.g., volatile memory, RAM, etc.). After storing the event tracking data communication, the process 300 then ends.

Although the above description describes that the process 300 determines an associated owner of the event tracking data communication at 320, one of ordinary skill in the art can appreciate that in some implementations this step is not performed (i.e., process 300 foregoes 310-330) and the process 300 immediately proceeds to 340 to store the event tracking data communication. In such implementations, the real-time server that receives the communication assumes ownership of the received communication.

The following section describes exemplar attributes and data structures of the real-time analytics data. An associated real-time server can temporarily store such attributes and data structures illustrated in FIG. 4 for reporting real-time analytics data.

FIG. 4 conceptually illustrates different attributes and data structures that are included in some configurations of the multi-tiered real-time analytics system. More specifically, FIG. 4 depicts exemplar attributes and data structures of a real-time analytics data structure 465 that are based on event tracking data communications. For instance, an event tracking data communication can include data for identifying a user session of a visitor, a location of the visitor, and other real-time analytics attributes. A real-time server can temporarily store the real-time analytics attributes and data structures shown in FIG. 4 based on the aforementioned data included in the event tracking data communication.

In some implementations, the real-time analytics data structure 465 can be understood as a container that is a collection of the various attributes and data structures based on one or more event tracking data communication(s). As shown in FIG. 4, the real-time analytics data structure 465 includes an associated web property identity (WPID) data structure 470, associated WPID attribute 475, hit data structure 480, hit data element 485 and hit real-time data attributes 490. Although these attributes and data structures are represented as included in a single data structure, some implementations can store each of the attributes and data structures in separate data structures that include any combination of data attributes. The aforementioned attributes and data structures are described in further detail as set forth below.

As illustrated, the associated web property identity (WPID) data structure 470 includes one or more associated WPID attributes. Each associated WPID attribute can represent a publisher of a web site which is associated with the real-time server. As shown, the associated WPID attribute 475 corresponds with a publisher with a “WPID 1” that represents a unique string ID for the publisher. One of ordinary skill in the art would understand that the unique string ID could be formed from any combination of alphanumeric characters and/or symbols.

In some implementations, each associated WPID attribute can include a reference to the hit data structure 480. The hit data structure 480 includes attributes for each event tracking data communication for the publisher. As shown, the hit data structure 480 includes hit data element 485 corresponding to a particular event tracking data communication “X” for the publisher corresponding with associated WPID attribute 475. The hit data element 485 is shown in more detail in breakout section 490.

As further shown in FIG. 4, the hit data element 485 can include the real-time data attributes 490 corresponding to a visitor ID, a last timestamp, location data and campaign data. The visitor ID attribute represents a unique ID string associated with a unique visitor session. The visitor ID attribute does not necessarily identify a particular end-user but instead corresponds with a particular user session on the publisher's web site. For example, the visitor ID can be a randomized identifier that is determined based on different information such as the visitor's web browser or client, the timestamp associated with the visitor's session, and/or the visitor's hostname, etc. Additionally, the location data does not identify the visitor's personal address, but is related to a geographical location (e.g., city) that can be determined based on the IP address assigned to the visitor (e.g., by an Internet service provider).

Next, the last timestamp attribute of the real-time data attributes 490 represents the most recent timestamp in which the unique visitor visited the publisher's web site. A timestamp in some implementations is a sequence of alphanumeric characters that denotes the date and/or time in which a particular event occurred. The location data attribute represents the geographical location of the unique visitor, such as a specified city, state, county, country, zip code, global positioning system coordinates, and/or geographical coordinates (e.g., latitude, longitude, elevation), etc. Lastly, the campaign attribute identifies a particular marketing campaign corresponding with a set of associated marketing activities. For instance, the associated marketing activities can respectively correspond to a set of web pages on the publisher's web site. Any visitor activity/action related to the set of web pages on the publisher's web page can then be associated with the marketing campaign. In this manner, the campaign attribute can identify the marketing campaign associated with the visitor action.

Different implementations can implement and organize the above described attributes and data structures in different ways. For instance, the real-time analytics data structure 465 of some configurations can be implemented as a collection of tree data structures, otherwise known as a “forest.” In this exemplar, a root node of a particular tree corresponds with a particular WPID (i.e., publisher identity) and each direct child of the root node can represent multiple visitor nodes that are associated with respective visitors to the publisher's web site. For each visitor node, visitor specific attributes are stored. Each visitor node can have multiple direct child nodes that represent the associated visitor's actions based on a timestamp. The timestamp of the last action for the associated visitor can also be stored with the parent associated visitor node described above. Alternatively or conjunctively, certain visitor attributes can be implemented as discrete nodes, such as a node including a city identifier with respective direct child nodes corresponding to a city name and geographical coordinates (e.g., latitude, longitude, etc.).

In the real-time analytics system, the subject technology can provide a push based system for event tracking data collection and reporting. For instance, the aggregated data can be routed/pushed through the real-time analytics system based on primary key sorting (e.g., based on a web property identity). After a front-end client submits an initial query with the real-time analytics system, a pipeline is established with the front-end client and event tracking data is pushed to the front-end client without requiring polling or other explicit requests from the front-end client. As incoming web site tracking data communications are received by a collecting server, the incoming web site tracking data communications are automatically routed and pushed through different tiers of the system. At each tier in the system, the web site tracking data can be processed (e.g., segmented, aggregated, and/or ranked, etc.) before being forwarded on to the next tier. In this manner, real-time web site tracking data is automatically presented to the front-end client without requiring constant refreshing (which introduces latency) of a web browser or similar client application.

FIG. 5 conceptually illustrates a process 500 for pushing event tracking data through different tiers of servers in a real-time analytics system. More specifically, the process 500 describes the communication flow through different tiers of the real-time analytics system (e.g., as illustrated in FIG. 1).

The process 500 begins at 505 by receiving an event tracking data communication at a first server. The first server in some configurations could be a logging server from the logging tier 120 as shown in FIG. 1 that receives the event tracking data communication from the collecting tier 110. Given the real-time constraints of the system, the event tracking data communication is pushed to the first server to meet a predetermined deadline. For instance, the predetermined deadline can be an operational deadline that a respective server in the real-time analytics system must respond by to process the event tracking data communication (e.g., by forwarding or further processing the communication).

The process 500 at 510 pushes the event tracking data communication from the first server to a real-time server. In some configurations, the real-time server aggregates the event tracking data for reporting. The process 500 pushes the event tracking data communication from the first server to the real-time server without a request from the real-time server. In this fashion, latency in the real-time analytics system is reduced because a typical request/response communication flow is avoided. The process 500 continues to 515 to identify a front-end client for receiving the aggregated event tracking data from the real-time server. In one example, the process 500 can identify the front-end client for receiving the aggregated event tracking data based on a session identifier associated with the front-end client.

At 520, the process 500 delivers the aggregated event tracking data to the identified front-end client for presentation. The process 500 delivers the aggregated event tracking data to the front-end client to meet a predetermined deadline as similarly discussed above. Again, in a push-based model, the process 500 delivers the aggregated event tracking data to the front-end client without a request from the front-end client. By way of example, the process 500 can utilize a persistent connection for delivering the aggregated event tracking data to the front-end client. Thus, the process 500 is not required to reestablish a new connection for each instance that event tracking data is reported to the front-end client. In some configurations, the front-end client can a web browser running on a client machine/device or a mobile application. The process 500 then ends.

Upon receiving the event tracking data communication, a real-time server can be assigned to process the event tracking data communication associated with a publisher's web site. To make this determination, the real-time server can consult a table, list or other suitable data structure that indicates one or more real-time servers that are associated with the publisher's web site. Moreover, the publisher's web site can correspond with a tracking account. As used herein, a tracking account can refer to an entity (e.g., web site publisher, company, user, etc.) acting as an account holder for the real-time analytics reporting provided by the real-time analytics system. A tracking account can be assigned a respective WPID that corresponds with an account holder's (e.g., publisher) web site, an application (e.g., web application), or representative of an event associated with the tracking account (e.g., such as people coming in and out of a store). The associated one or more real-time servers therefore can be considered an “owner” of any event tracking data communication for that publisher's web site based on the tracking account. In practical terms, when acting as the owner, the associated real-time server is required to process each event tracking data communication for that publisher's web site.

In some instances, however, the real-time server receiving the event tracking data communication is not associated with the communication. This can occur when the real-time server receiving the communication is not associated with a web site of a particular publisher (e.g., not an owner). Therefore, the real-time server is not considered an owner of the event tracking data communication and should forward the communication to a different real-time server acting as the owner. To implement the push-based model for providing real-time analytics data, the real-time server can push the event tracking data communication to another real-time server acting as the owner of the event tracking data communication associated with the publisher's web site (e.g., the tracking account described above).

FIG. 6 conceptually illustrates an exemplary process 600 for pushing event tracking data to a designated real-time server acting as an owner. A real-time server can implement the process 600 to forward event tracking data communication to a different real-time server(s) acting as the owner of the event tracking data communication. In some configurations, process 600 can be performed conjunctively with the process 500 described in connection with FIG. 5.

The process 600 begins at 605 when an event tracking data communication is received. The process 600 at 610 determines whether a tracking account associated with the event tracking data communication has an owner. The owner as described before can be a real-time server assigned to process incoming event tracking data communications associated with the tracking account. In some configurations, the tracking account can be determined based on a web property identity corresponding with the tracking account.

After determining the owner for the tracking account, the process 600 at 615 determines whether the event tracking data communication has a different owner (from the real-time server receiving the communication). If the event tracking data communication does not have a different owner, the process 600 ends. Alternatively, the process 600 continues to 620 if the process 600 determines that the event tracking data communication has a different owner corresponding to a different real-time server.

At 620, the process 600 determines an elapsed time period since receiving the event tracking data communication if the owner is a different real-time server. In one example, the process 600 can determine the elapsed time period based on a timestamp associated with the event tracking data communication. In another example, the process 600 determines the elapsed time period based on a time when the event tracking data communication is received by the real-time server.

At 625, the process 600 determines whether the elapsed time period is at least a threshold time period. In some configurations, the threshold time period is an operational deadline for pushing the event tracking data communication to the different real-time server. Given the real-time constraints in the real-time analytics system, the real-time server can discard the event tracking data communication (along with the event tracking data included therein) if the operational deadline can not be met. Thus, the real-time analytics system can tolerate a degree of data loss in reporting the real-time analytics data in order to maintain low latency for reporting the data. By way of example, the threshold time period is one second for pushing the event tracking data communication to the different real-time server in some configurations. If the process 600 determines that the elapsed time period is at least the threshold time period then the process 600 at 630 discards the event tracking data communication. Alternatively, the process 600 can push the event tracking data communication to the different real-time server if the elapsed time period is below the threshold time period. The process 600 then ends.

The following section describes a software architecture of a real-time server that can implement the above described processes and data structures as a computer program running on a particular machine, such as a computer, or stored as instructions in a computer readable medium.

FIG. 7 conceptually illustrates an exemplary software architecture of a real-time server 705 of some configurations. In particular, FIG. 7 shows various modules and components of the real-time server 705 for implementing the above described processes in FIGS. 3, 5 and 6 and the data structures in FIG. 4. In some configurations, the software architecture of the real-time server is part of a server-based implementation running a particular machine connected to a network.

As illustrated in FIG. 7, the real-time server 705 can include a request router module 710 for parsing and routing web site tracking data, a hit accumulator module 720 for buffering and querying real-time analytics data, and a real-time analytics data storage 730 for temporarily storing real-time analytics data. The real-time analytics data storage 730 in some implementations is a type of volatile and non-persistent storage (e.g., RAM). The operation of the aforementioned modules of the real-time server 705 are discussed in the following description.

The real-time server 705 can receive web site tracking data 702 in a request router module 710. The web site tracking data 702 can be in the form of an event tracking data communication that is received over a network 750. As shown, the request router module 710 includes a parser module 712 and an ownership module 714. The parser module 712 of some implementations can parse the web site tracking data to extract real-time analytics data and to subsequently transmit the real-time analytics data to the ownership module 714. The ownership module 714 determines a set of associated real-time servers that are owners of the web site tracking data corresponding with a publisher's web site. In an instance where the ownership module 714 determines that the web site tracking data 702 is not associated with the real-time server 705, the request router module 710 can route the web site tracking data 702 over the network 750 to other associated real-time servers. As shown in FIG. 7, additional real-time servers 760 and 770 are connected to the network 750. Alternatively, in the event that the ownership module 714 determines that the real-time server 705 is associated with the web site tracking data 702, the request router module 710 routes (i.e., sends) the web site tracking data to the hit accumulator module 720 for further processing and temporary storage.

Additionally, the request router module 710 is configured to route the event tracking data communication to a different real-time server if the associated owner is determined to be the different real-time server by the ownership module 714. In some configurations, the request router module 710 is configured to determine an elapsed time period since receiving the event tracking data communication. The request router module 714 can discard the event tracking data communication if the elapsed time period is at least a threshold time period. Alternatively, the request router module 714 is configured to push the event tracking data communication to the different real-time server if the elapsed time period is below the threshold time period.

The hit accumulator module 720 receives the web site tracking data routed from the request router module 710. In some implementations, the hit accumulator module 720 includes a buffer module 722 as shown for queuing/aggregating the web site tracking data. The buffer module 722 can be implemented in different ways. For instance, the buffer module 722 could utilize a first-in first-out (FIFO) scheme in which the earliest received web site tracking data is transferred out before later received data. In other instances, the buffer module 722 could employ a last-in first-out (LIFO) scheme that is analogous to a stack in which the later received data is transferred out before earlier received data.

As shown in FIG. 7, the buffer module 722 then sends web site tracking data to a real-time analytics query engine 724 in the hit accumulator module 720. The real-time analytics query engine 724 of some configurations performs query processing and data management of the real-time analytics data storage 730. By way of example, the real-time analytics query engine 724 can perform write and/or update operations to temporarily store the web site tracking data to the real-time analytics data storage 730. Given the requirements of low latency involved with reporting real-time analytics data, the real-time analytics data storage 730 can be implemented to provide fast access to stored real-time analytics data. In particular, the real-time analytics data storage 730 can be implemented as fast non-persistent/volatile memory that is periodically flushed and/or overwritten with more recent web site tracking data. In this manner, the overhead of managing the data in the real-time analytics data storage 730 is reduced because longevity of the data is not a primary objective.

In some implementations, the hit accumulator module 720 can execute queries for sorting and/or aggregating the received web site tracking data for temporary storage in the real-time analytics data storage 730. For instance, referring back to FIG. 1, a real-time server from the real-time analytics tier 140 can aggregate and sort the real-time analytics data according to one or more predetermined queries for temporary storage and then send the aggregated and/or sorted real-time analytics data to the front-end tier 150 for reporting the real-time analytics data. As shown in FIG. 7, the real-time analytics query engine 724 of hit accumulator module 720 can execute the queries in order to sort and/or aggregate the received web site tracking data before temporarily storing the real-time analytics data in the real-time analytics data storage 730. The real-time server 705 can then send the stored aggregated and/or sorted real-time analytics data to a front-end server for reporting and/or presenting real-time analytics data to an end-user.

The following section describes an exemplary system that implements aspects of the above described invention.

Many of the above-described features and applications are implemented as software processes that are specified as a set of instructions recorded on a machine readable storage medium (also referred to as computer readable medium). When these instructions are executed by one or more processing unit(s) (e.g., one or more processors, cores of processors, or other processing units), they cause the processing unit(s) to perform the actions indicated in the instructions. Examples of machine readable media include, but are not limited to, CD-ROMs, flash drives, RAM chips, hard drives, EPROMs, etc. The machine readable media does not include carrier waves and electronic signals passing wirelessly or over wired connections.

In this specification, the term “software” is meant to include firmware residing in read-only memory and/or applications stored in magnetic storage, which can be read into memory for processing by a processor. Also, in some implementations, multiple software inventions can be implemented as sub-parts of a larger program while remaining distinct software inventions. In some implementations, multiple software inventions can also be implemented as separate programs. Finally, any combination of separate programs that together implement a software invention described here is within the scope of the invention. In some implementations, the software programs, when installed to operate on one or more systems, define one or more specific machine implementations that execute and perform the operations of the software programs.

A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

FIG. 8 conceptually illustrates a system 800 with which some implementations of the subject technology may be implemented. The system 800 can be a computer, phone, PDA, or any other sort of electronic device. Such a system includes various types of computer readable media and interfaces for various other types of computer readable media. The system 800 includes a bus 805, processing unit(s) 810, a system memory 815, a read-only memory 820, a storage device 825, an optional input interface 830, an optional output interface 835, and a network interface 840.

The bus 805 collectively represents all system, peripheral, and chipset buses that communicatively connect the numerous internal devices of the system 800. For instance, the bus 805 communicatively connects the processing unit(s) 810 with the read-only memory 820, the system memory 815, and the storage device 825.

From these various memory units, the processing unit(s) 810 retrieves instructions to execute and data to process in order to execute the processes of the invention. The processing unit(s) can be a single processor or a multi-core processor in different implementations.

The read-only-memory (ROM) 820 stores static data and instructions that are needed by the processing unit(s) 810 and other modules of the system 800. The storage device 825, on the other hand, is a read-and-write memory device. This device is a non-volatile memory unit that stores instructions and data even when the system 800 is off. Some implementations of the invention use a mass-storage device (such as a magnetic or optical disk and its corresponding disk drive) as the storage device 825.

Other implementations use a removable storage device (such as a flash drive, a floppy disk, and its corresponding disk drive) as the storage device 825. Like the storage device 825, the system memory 815 is a read-and-write memory device. However, unlike storage device 825, the system memory 815 is a volatile read-and-write memory, such a random access memory. The system memory 815 stores some of the instructions and data that the processor needs at runtime. In some implementations, the invention's processes are stored in the system memory 815, the storage device 825, and/or the read-only memory 820. For example, the various memory units include instructions for processing multimedia items in accordance with some implementations. From these various memory units, the processing unit(s) 810 retrieves instructions to execute and data to process in order to execute the processes of some implementations.

The bus 805 also connects to the optional input and output interfaces 830 and 835. The optional input interface 830 enables the user to communicate information and select commands to the system. The optional input interface 830 can interface with alphanumeric keyboards and pointing devices (also called “cursor control devices”). The optional output interface 835 can provide display images generated by the system 800. The optional output interface 835 can interface with printers and display devices, such as cathode ray tubes (CRT) or liquid crystal displays (LCD). Some implementations can interface with devices such as a touchscreen that functions as both input and output devices.

Finally, as shown in FIG. 8, bus 805 also couples system 800 to a network interface 840 through a network adapter (not shown). In this manner, the computer can be a part of a network of computers (such as a local area network (“LAN”), a wide area network (“WAN”), or an Intranet, or an interconnected network of networks, such as the Internet. Any or all components of system 800 can be used in conjunction with the invention.

These functions described above can be implemented in digital electronic circuitry, in computer software, firmware or hardware. The techniques can be implemented using one or more computer program products. Programmable processors and computers can be included in or packaged as mobile devices. The processes and logic flows can be performed by one or more programmable processors and by one or more programmable logic circuitry. General and special purpose computing devices and storage devices can be interconnected through communication networks.

Some implementations include electronic components, such as microprocessors, storage and memory that store computer program instructions in a machine-readable or computer-readable medium (alternatively referred to as computer-readable storage media, machine-readable media, or machine-readable storage media). Some examples of such computer-readable media include RAM, ROM, read-only compact discs (CD-ROM), recordable compact discs (CD-R), rewritable compact discs (CD-RW), read-only digital versatile discs (e.g., DVD-ROM, dual-layer DVD-ROM), a variety of recordable/rewritable DVDs (e.g., DVD-RAM, DVD-RW, DVD+RW, etc.), flash memory (e.g., SD cards, mini-SD cards, micro-SD cards, etc.), magnetic and/or solid state hard drives, read-only and recordable Blu-Ray® discs, ultra density optical discs, any other optical or magnetic media, and floppy disks. The computer-readable media can store a computer program that is executable by at least one processing unit and includes sets of instructions for performing various operations. Examples of computer programs or computer code include machine code, such as is produced by a compiler, and files including higher-level code that are executed by a computer, an electronic component, or a microprocessor using an interpreter.

While the above discussion primarily refers to microprocessor or multi-core processors that execute software, some implementations are performed by one or more integrated circuits, such as application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs). In some implementations, such integrated circuits execute instructions that are stored on the circuit itself.

As used in this specification and any claims of this application, the terms “computer”, “server”, “processor”, and “memory” all refer to electronic or other technological devices. These terms exclude people or groups of people. For the purposes of the specification, the terms display or displaying means displaying on an electronic device. As used in this specification and any claims of this application, the terms “computer readable medium” and “computer readable media” are entirely restricted to tangible, physical objects that store information in a form that is readable by a computer. These terms exclude any wireless signals, wired download signals, and any other ephemeral signals.

To provide for interaction with a user, implementations of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user's client device in response to requests received from the web browser.

Configurations of the subject matter described in this specification can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), an inter-network (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).

The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some configurations, a server transmits data (e.g., an HTML page) to a client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device). Data generated at the client device (e.g., a result of the user interaction) can be received from the client device at the server.

It is understood that any specific order or hierarchy of steps in the processes disclosed is an illustration of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged, or that all illustrated steps be performed. Some of the steps may be performed simultaneously. For example, in certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the configurations described above should not be understood as requiring such separation in all configurations, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not intended to be limited to the aspects shown herein, but is to be accorded the full scope consistent with the language claims, wherein reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more.” Unless specifically stated otherwise, the term “some” refers to one or more. Pronouns in the masculine (e.g., his) include the feminine and neuter gender (e.g., her and its) and vice versa. Headings and subheadings, if any, are used for convenience only and do not limit the invention.

A phrase such as an “aspect” does not imply that such aspect is essential to the subject technology or that such aspect applies to all configurations of the subject technology. A disclosure relating to an aspect may apply to all configurations, or one or more configurations. A phrase such as an aspect may refer to one or more aspects and vice versa. A phrase such as a “configuration” does not imply that such configuration is essential to the subject technology or that such configuration applies to all configurations of the subject technology. A disclosure relating to a configuration may apply to all configurations, or one or more configurations. A phrase such as a configuration may refer to one or more configurations and vice versa.

The word “exemplary” is used herein to mean “serving as an example or illustration.” Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs.

All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. 

What is claimed is:
 1. A method for routing an event tracking data comprising: collecting from a website, at a collecting tier, an event tracking data for the website, wherein the event tracking data comprises visitor data that is obtained from a user device in response to actions performed on the user device with respect to the website loaded in a browser of the user device; pushing, from the collecting tier, the event tracking data to a logging tier based on a predetermined deadline; selecting, at a logging server of the logging tier, a first real-time server of a real-time tier; pushing, from the logging server, the event tracking data to the first real-time server based on the predetermined deadline, the first real-time server comprising a data structure indicating whether the first real-time server or a second real-time server of the real-time tier is assigned to process the event tracking data for the website; determining, at the first real-time server, whether the second real-time server of the real time tier is assigned to process the event tracking data for the website based on the data structure, wherein being assigned to process the event tracking data for the website comprises being assigned to generate analytics data based on the visitor data of the event tracking data and report the analytics data to a front-end server; comparing, at the first real-time server, a threshold time period with an elapsed time of the event tracking data; pushing, in response to if the comparison showing the elapsed time as less than the threshold time and in response to determining that the second real-time server is assigned to process the event tracking data for the website, the event tracking data from the first real-time server to the second real-time server based on the predetermined deadline; or discarding, if the comparison shows the elapsed time greater than the threshold time, the pushed event tracking data.
 2. The method of claim 1, wherein determining that the second real-time server is assigned to process the event tracking data comprises: determining a tracking account associated with the event tracking data; and determining that the second real-time server is associated with the tracking account.
 3. The method of claim 1, wherein determining that the second real-time server is assigned to process the event tracking data comprises: determining a web property identity associated with the event tracking data; determining a tracking account associated with the web property identity; and determining that the second real-time server is associated with the tracking account.
 4. The method of claim 1, further comprising calculating the elapsed time based on when the first real-time server received the event tracking data.
 5. The method of claim 1, further comprising calculating the elapsed time based on a timestamp associated with the event tracking data.
 6. The method of claim 1, wherein pushing the event tracking data to the second real-time server further comprises pushing the event tracking data without a request or polling.
 7. The method of claim 1, wherein the predetermined deadline comprises a specified period of time by which the event tracking data must be processed at each respective tier.
 8. The method of claim 1, wherein selecting a first real-time server comprises randomly selecting, at a logging server of the logging tier, a real-time server of a real-time tier.
 9. The method of claim 1, wherein selecting a first real-time server comprises selecting, at a logging server of the logging tier, a real-time server of a real-time tier based on load-balancing requirements among a plurality of real-time servers of the real-time tier.
 10. The method of claim 1, further comprising: receiving, at the first real-time server of the real-timer tier, a second event tracking data; determining, at the first real-time server, that the first real-time server is acting as an owner of the second event tracking data; and processing, at the first real-time server, the second event tracking data.
 11. A system for routing an event tracking data, comprising: a collecting tier, in connection with a network and a logging tier, the collecting tier comprising a collecting server including a processor executing the steps of: collecting, from a website, an event tracking data for the website wherein the event tracking data comprises visitor data that is obtained from a user device in response to actions performed on the user device with respect to the website loaded in a browser of the user device; pushing the event tracking data to a logging tier based on a predetermined deadline; the logging tier, in connection with the collection tier and a real-time tier, the logging tier comprising a logging server including a processor executing the steps of: selecting a first real-time server of the real-time tier; pushing the event tracking data to the first real-time server based on the predetermined deadline, the first real-time server comprising a data structure indicating whether the first real-time server or a second real-time server of the real-time tier is assigned to process the event tracking data for the website; the real-time tier, in connection with the logging tier, the real-time tier comprising the first and the second real-time servers, the first real-time server including a processor executing the steps of: determining whether the second real-time server of the real time tier is assigned to process the event tracking data for the website based on the data structure, wherein being assigned to process the event tracking data for the website comprises being assigned to generate analytics data based on the visitor data of the event tracking data and report the analytics data to a front-end server; comparing a threshold time period with an elapsed time of the event tracking data; pushing, in response to if the comparison showing the elapsed time as less than the threshold time and in response to determining that the second real-time server is assigned to process the event tracking data for the website, the event tracking data from the first real-time server to the second real-time server based on the predetermined deadline; or discarding, if the comparison shows the elapsed time greater than the threshold time, the pushed event tracking data.
 12. The system of claim 11, wherein determining that the second real-time server is assigned to process the event tracking data comprises: determining a tracking account associated with the event tracking data; and determining that the second real-time server is associated with the tracking account.
 13. The system of claim 11, wherein determining that the second real-time server is assigned to process the event tracking data comprises: determining a web property identity associated with the event tracking data; determining a tracking account associated with the web property identity; and determining that the second real-time server is associated with the tracking account.
 14. The system of claim 11, wherein the first real-time server further comprises calculating the elapsed time based on when the first real-time server received the event tracking data.
 15. The system of claim 11, wherein the first real-time server further comprises calculating the elapsed time based on a timestamp associated with the event tracking data.
 16. The system of claim 11, wherein the first real-time server pushing the event tracking data to the second real-time server further comprises pushing the event tracking data without a request or polling.
 17. The system of claim 11, wherein the predetermined deadline comprises a specified period of time by which the event tracking data must be processed at each respective tier.
 18. The system of claim 11, wherein the logging tier selecting a first real-time server comprises randomly selecting, at a logging server of the logging tier, a real-time server of a real-time tier.
 19. The system of claim 11, wherein the logging tier selecting a first real-time server comprises selecting, at a logging server of the logging tier, a real-time server of a real-time tier based on load-balancing requirements among a plurality of real-time servers of the real-time tier.
 20. The system of claim 11, wherein the first real-time server further comprises: receiving a second event tracking data; determining that the first real-time server is acting as an owner of the second event tracking data; and processing the second event tracking data. 